Semi-supervised learning of a nonnative phonetic contrast: How much feedback is enough?

Abstract

Semi-supervised learning refers to learning that occurs when feedback about performance is provided on only a subset of training trials. Algorithms for semi-supervised learning are popular in machine learning because of their minimal reliance on labeled data. There have been, however, only a few reports of semi-supervised learning in humans. Here we document human semi-supervised learning on a nonnative phonetic classification task. Classification performance remained unchanged when 60 feedback trials were provided on each of the two days of training. In contrast, performance improved when 60 feedback trials were combined with 240 no-feedback trials each day. In variants of this successful semi-supervised regimen, increasing the daily number of feedback trials from 60 to 240 did not increase the amount of learning, while decreasing that number to 30 abolished learning. Finally, replacing the no-feedback trials with stimulus exposure alone had little effect on the outcome. These results were an unexpected consequence of combining training periods with feedback and testing periods without feedback, illustrating that no-feedback testing can influence learning outcomes. More broadly, these data suggest that task performance with feedback can function as an all-or-none trigger for recruiting the contribution of trials without feedback, or mere stimulus exposures, to human learning.